In CanCORS, we assembled a population-based cohort of approximately 10,000 patients with newly diagnosed lung and colorectal cancer and followed them for 15 months after diagnosis. This study has already yielded important new insights into the quality of cancer care in the United States as well as the short-term outcomes of cancer and cancer treatment in the community, with many additional analyses expected in the near future. However, the full benefit of this substantial investment in collection of meticulous data on patients'primary cancer therapy can only be realized with additional follow-up of the cohort. In CanCORS II, we propose to leverage this investment by studying patients who survived beyond 15 months to assess several key domains that could not be addressed in CanCORS I. These include: outcomes and quality of care among survivors;dissemination of new, targeted cancer therapies;outcomes and quality of care among patients with recurrent advanced disease;and finally, the association of care processes and disease-related outcomes in the community setting. We have designed a targeted data collection strategy that capitalizes on the scientific opportunities available from study of this unique cohort, but that's also efficient. The data collection components include: a survey of disease-free survivors;a survey of patients with advanced disease (or their surrogates);a survey of patients'providers;abstraction of medical records of patients with advanced disease;and linkage with Medicare claims, automated data from the VA and HMOs, the Social Security Death Index, and the National Death Index. Analyses will be driven by a conceptual framework that distinguishes between the patient, provider, and health system factors that may be associated with processes and outcomes of care. Particular attention will be paid to potentially mutable factors so that results directly inform clinical and policy efforts to improve the outcomes of patients with the two most deadly forms of cancer in the US. RELEVANCE (See instructions):